School of Public Administration, Southwestern University of Finance and Economics, Chengdu, China.
School of Finance, Shanghai Lixin University of Accounting and Finance, Shanghai, China.
Sci Data. 2022 May 12;9(1):202. doi: 10.1038/s41597-022-01322-5.
As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992-2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992-2019 based on our calibrated nighttime light data.
作为基本数据，国内生产总值（GDP）和用电量可用于有效评估经济状况和居民生活水平。一些学者已经估算了网格化 GDP 和用电量。然而，这种网格化数据存在一些缺点，包括高估实际 GDP 增长率、忽略了网格时空动态的异质性以及时间跨度有限。同时，这些研究中采用的国防气象卫星计划的操作扫描线系统（DMSP/OLS）和国家极地轨道伙伴关系的可见红外成像辐射计（NPP/VIIRS）夜间灯光数据作为代理工具，仍然存在不完善的匹配结果、时空变化不连续等缺点。在本研究中，我们采用了一系列方法，如粒子群优化-反向传播（PSO-BP）算法，来统一 DMSP/OLS 和 NPP/VIIRS 图像的尺度，并获得 1992-2019 年连续的 1km×1km 网格化夜间灯光数据。随后，我们从修正的实际增长角度出发，采用自上而下的方法，根据我们校准的夜间灯光数据，计算了 1992-2019 年全球 1km×1km 网格化修正实际 GDP 和用电量。
